class_docstr: Lexicon acquisition. produce opinion lexicon and an aspect lexicon based
  on input dataset.
  
  Attributes:
      opinion_candidate_list_curr_iter (dict): candidate opinion terms in the current iteration
      opinion_candidate_list_prev_iter (dict): opinion candidates list of previous iteration
      opinion_candidate_list (dict): opinion terms learned across all iterations
      opinion_candidates_list_final (list): final opinion candidates list
      opinion_candidate_list_raw (dict): all instances of candidate opinion terms
                                         across all iterations
      aspect_candidate_list_curr_iter (dict): candidate terms in the current iteration
      aspects_candidate_list_prev_iter(list): Aspect candidates list of previous iteration
      aspect_candidate_list (list):  aspect terms learned across all iterations
      aspect_candidates_list_final (list): final aspect candidates list
      aspect_candidate_list_raw (dict): all instances of candidate aspect terms
                                        across all iterations
code: def __init__(self, asp_thresh=3, op_thresh=2, max_iter=1):
      self.opinion_candidate_list_prev_iter = (
          read_generic_lex_from_file(
              AcquireTerms.generic_opinion_lex_path
          )
      )
      self.generic_sent_dict = copy.deepcopy(
          self.opinion_candidate_list_prev_iter
      )
      self.opinion_candidate_list = {}
      self.opinion_candidate_list_raw = {}
      self.opinion_candidate_list_curr_iter = {}
      self.opinion_candidates_list_final = []
      self.aspect_candidate_list_raw = {}
      self.aspect_candidate_list = list()
      self.aspect_candidate_list_curr_iter = {}
      self.aspect_candidates_list_final = []
      self.init_aspect_dict = list()
      self.aspects_candidate_list_prev_iter = list()
      self.min_freq_aspect_candidate = asp_thresh
      self.min_freq_opinion_candidate = op_thresh
      self.max_num_of_iterations = max_iter
  
imports: import copy
  import re
  import sys
  from os import PathLike
  from tqdm import tqdm
  from nlp_architect.models.absa import TRAIN_LEXICONS, LEXICONS_OUT
  from nlp_architect.models.absa import GENERIC_OP_LEX
  from nlp_architect.models.absa.inference.data_types import Polarity
  from nlp_architect.models.absa.train.data_types import AspectTerm, DepRelation, DepRelationTerm, LoadOpinionStopLists, LoadAspectStopLists, OpinionTerm, QualifiedTerm
  from nlp_architect.models.absa.train.rules import rule_1, rule_2, rule_3, rule_4, rule_5, rule_6
  from nlp_architect.models.absa.utils import _load_parsed_docs_from_dir, _write_final_opinion_lex, _load_lex_as_list_from_csv, read_generic_lex_from_file
label_desc: Use image node_id 1 to create a new AcquireTerms object from inherited base classes: object with example: obj = AcquireTerms(asp_thresh, op_thresh, max_iter)